The SegGPT model was proposed in SegGPT: Segmenting Everything In Context by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. SegGPT employs a decoder-only (GPT-like) Transformer that can generate a segmentation mask given an input image, a prompt image and its corresponding prompt mask. The model achieves remarkable one-shot results with 56.1 mIoU on COCO-20 and 85.6 mIoU on FSS-1000. This work is supported by the National Science and Technology Major Project (No. 2022ZD0116300). 本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116300）支持。
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View details ↗The SegVol is a universal and interactive model for volumetric medical image segmentation. SegVol accepts point, box, and text prompts while output volumetric segmentation. By training on 90k unlabeled Computed Tomography (CT) volumes and 6k labeled CTs, this foundation model supports the segmentation of over 200 anatomical categories. We have released 25 open source datasets(M3D-Seg) for training SegVol, and these preprocessed data have been uploaded to ModelScope and HuggingFace. You can use the following script to easily load cases and insert them into Test script and Training script. 我们已经发布了用于训练SegVol的25个开源数据集(M3D-Seg),并将预处理后的数据上传到了ModelScope和HuggingFace。 您可以使用下面的script方便地载入，并插入到Test script和Training script中。
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View details ↗This is the model card for the Findings of EMNLP 2021 paper REBEL: Relation Extraction By End-to-end Language generation. We present a new linearization approach and a reframing of Relation Extraction as a seq2seq task. The paper can be found here. If you use the code, please reference this work in your paper: @inproceedings{huguet-cabot-navigli-2021-rebel-relation, title = "{REBEL}: Relation Extraction By End-to-end Language generation", author = "Huguet Cabot, Pere-Llu{\'\i}s and Navigli, Roberto", booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021", month = nov, year = "2021", address = "Punta Cana, Dominican Republic", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.findings-emnlp.204", pages = "2370--2381", abstract = "Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks. In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model{'}s flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.", } Be aware that the inference widget at the right does not output special tokens, which are necessary to distinguish the subject, object and relation types. For a demo of REBEL and its pre-training dataset check the Spaces demo.
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View details ↗This is the model card for the EMNLP 2021 paper WikiNEuRal: Combined Neural and Knowledge-based Silver Data Creation for Multilingual NER. We fine-tuned a multilingual language model (mBERT) for 3 epochs on our WikiNEuRal dataset for Named Entity Recognition (NER). The resulting multilingual NER model supports the 9 languages covered by WikiNEuRal (de, en, es, fr, it, nl, pl, pt, ru), and it was trained on all 9 languages jointly. The original repository for the paper can be found at https://github.com/Babelscape/wikineural. This model is trained on WikiNEuRal, a state-of-the-art dataset for Multilingual NER automatically derived from Wikipedia. Therefore, it might not generalize well to all textual genres (e.g. news). On the other hand, models trained only on news articles (e.g. only on CoNLL03) have been proven to obtain much lower scores on encyclopedic articles. To obtain more robust systems, we encourage you to train a system on the combination of WikiNEuRal with other datasets (e.g. WikiNEuRal + CoNLL).
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